Presentation on theme: "1 Sector-Specific Technical Change Susanto Basu Boston College and NBER Jonas Fisher Federal Reserve Bank of Chicago John Fernald Federal Reserve Bank."— Presentation transcript:
1 Sector-Specific Technical Change Susanto Basu Boston College and NBER Jonas Fisher Federal Reserve Bank of Chicago John Fernald Federal Reserve Bank of San Francisco Miles Kimball University of Michigan and NBER
Objective: Measure technology by final-use sector (esp. consumption versus equipment investment) Idea: Estimate technology residuals from industry data, then aggregate through the input-output tables Contribution: Do not identify final-use technology from relative price data Makes our method more general than existing literature We can test (and reject) the assumptions needed for relative price approach to work Robust to differing factor shares, time-varying markups, sticky prices, variable factor utilization, increasing returns, changing tax rates, …
Using input-output tables to map disaggregated technology shocks into final-use technology Direct technology estimates from industry production functions vector dz of (gross-output) technology shocks, [dz 1, dz 2, …] Implicit production function for delivering output to final consumption or investment. Intuition: Matrix B is (nominal) intermediate input shares b ij is share of commodity j in producing commodity i Technology for deliveries to final demand Weight by final-use shares, e.g.: 3
44 Given TFP for final-use commodities, Z C, Z J, etc. easy
What is net exports technology In data, have to confront that economy is open Some commodity supply is imported Purpose of exports is to import (allowing use of those commodities) Technology: Terms of trade Final-use net-exports technology captures ability to obtain imports from exports Terms of trade improvements Technology improvements in goods we export 5
66 Two issues arise in input-output data to measure relevant intermediate-input matrix B Final use is by commodity, productivity data (dz i ) are by industry I-O make table maps commodity production to industries Final-use is from total commodity supply, not domestic production I-O use table tells us both production and imports
77 What does an input-output use table look like? Columns give inputs into domestic production Rows give uses of the commodity Nominal commodity-by-commodity use table
88 We define a trade goods commodity, which uses commodity exports as an input to produce imports Nominal commodity-by-commodity use table Exports represent intermediate inputs into trade-goods production. Imports are used as intermediate inputs to produce commodity supply
99 We define a trade goods commodity, which uses commodity exports as an input to produce imports Nominal commodity-by-commodity use table Exports represent intermediate inputs into trade-goods production. Imports are used as intermediate inputs to produce supplies of other commodities
10 We define a trade goods commodity, which uses commodity exports as an input to produce imports Nominal commodity-by-commodity use table Exports represent intermediate inputs into trade-goods production. Imports are used as intermediate inputs to produce commodity supply
11 Net exports are one use of trade goods, representing a claim on future imports Nominal commodity-by-commodity use table NX are a form of final expenditure, much like investment.
12 Tables now add up, in terms of commodity supply! Nominal commodity-by-commodity use table
13 Start with KLEM productivity data from Jorgenson et al. Key collaborators include Fraumeni, Ho, Stiroh, Gollop, and others Annual input-output tables underlying these productivity data industries/commodities Includes final use, which allows us to distinguish ND-S Consumption (dont have owner-occ housing) Consumer Durables Government purchases of G&S (not govt administration) Equipment investment Structures investment Exports and Imports
14 We modify original data to incorporate alternative deflators for durable goods Key work of Gordon (1983), updated by Cummins-Violante (2002) New deflators redefine output for each industry Aggregate using I-O tables to get new measures of C, I, etc. Of course, also new prices for each category of expenditure
Need vector of industry technology innovations Production function Could use industry Solow residuals: Concerns: Non-constant returns unobserved variations in labor effort E i and capitals workweek S i Thus use BFK (2006, AER) purified Solow residuals instead 15
16 Feeding industry BFK shocks through I-O tables: Equip and con. dur. technology rise fastest Cumulated log change in final-use BFK technology
Consumption and investment technology (exogenous componentzero out ToT, natural resources) 17
18 Relative sectoral technology diverges from typical macro proxy of relative prices Relative price changes have correlation (in annual data) of only 0.23 with relative BFK technology Correlation of growth in relative TFP, relative BFK technology, and relative output prices Relative TFP Relative BFK Technology Relative Final- goods prices Relative TFP (dz Equipment – dz Consumption ) 1 Relative BFK Technology (dz BFK, Equipment – dz BFK, Consumption ) Relative final-goods prices (dp Consumption – dp Equipment )
19 Relative prices respond to relative technology with long lags 19 Relative Price (LHS var): growth in price of consumption (ND and services) relative to price of equipment Rel. Technology (RHS var) : Growth in equipment technology relative to consumption (ND and services) d Relative Technology (Lag)Cumulative 0123effect d Relative Price (cons to equip) (0.13)(0.20)(0.14)(0.19)(0.31)
20 Conclusions Theory suggests that final use sector where technology change occurs matters for its effects E.g., consumption-technology neutrality, 2-sector sticky-price model Measure sectoral technical change using a new method that doesnt require relative prices Gives similar long-run results, but very different short-run implications Effects of sector-specific technology shocks look like business cycles Equipment technology improvements reduce output, hours, investment, and consumption Consumption technology improvement raise output, hours, investment, and consumption
22 Equipment investment technology and consumption technology have very different macroeconomic effects 22 Each row is a separate regression of log change in variable shown on current and lagged tech shocks Equip tech. includes con dur and govt equip. Cons. (Nondur) tech includes structures and nonequip. govt. Intrumental variables estimation. Instruments zero out terms of trade and industry shocks not estimated via BFK. Annual data
23 Technology shocks explain a lot of the variation in equipment… Corr = 0.59
24 …as well as hours Corr = 0.64
25 Empirical Implications: Low EIS and Permanent Tech Shocks With permanent technology shocks and King-Plosser-Rebelo utility and relatively low elasticity of intertemporal substitution ( 0.3), investment technology shocks also have very little immediate effects on labor hours, though they do raise investment in a way that consumption technology shocks do not.
27 Comments With log preferences, ln(A) is additively-separable: Any stochastic process for A has no effect on optimal decision rules for N, X and I. More general King-Plosser-Rebelo preferences: If A follows a geometric random walk it has no effect on optimal decision rules for N, X and I.
28 What is technology? Is technology the economys PPF? The change in production functions for domestic C and I? We use the first, broader, definition.
29 Notes Trade technology is the terms of trade Suppose there are no intermediate-inputs and one of each final-use commodity (e.g., a single consumption good) Final-use technology is technology in that commodity Our definition is correct for typical two-sector macro model Otherwise, takes account of intermediate-input flows If all sectors face same input prices and have identical factor shares (including intermediates), then relative final-goods prices reflect relative technologies Again, our definition is correct for typical special cases used in macro (e.g., Greenwood, Hercowitz, Krusell)
30 Two issues arise in using industry/commodity data to measure final-use sector technical change Final use is by commodity, productivity data are by industry I-O make table maps commodity production to industries We assume: MAKE*dz Commodity = dz Industry, Final-use is from total commodity supply, not domestic production I-O use table tells us both production and imports
31 We aggregate commodity technology shocks to final uses with constant-share aggregation
32 Motivation: In benchmark RBC model, consumption- technology shocks are neutral Suppose utility is logarithmic U = ln(C) – v(L) Let A be multiplicative technology for producing non-durable consumption Consumption-technology neutrality proposition: In two-sector RBC model, stochastic process for A does not affect labor hours L, investment J, or the quantity of resources devoted to producing consumption goods (X) A affects only production of nondurable consumption goods
33 Social-planners problem for two-sector growth model, with CRS, identical production technologies
34 This is special case of following problem, where A t is additively separable, and thus doesnt affect decision rules
35 This is special case of following problem, where A t is additively separable, and thus doesnt affect decision rules Equivalent problem: Empirically, do shocks to different final sectors have different economic effects?
36 Relative sector-specific TFP reflects relative output prices and relative primary input prices Primal (standard) TFP for commodity i equals dual: In vector/matrix form: Intermediate input prices are s N dn Bdp, so:
37 What we do instead Seek a more robust way to measure relative technology Use industry data to estimate underlying shocks Production-function regressions a la BFK (2006) Then aggregate using I-O tables to final-use technology changes for C, I, etc. Present findings, implications for business-cycle models
38 Outline 1.Introduction: Declining relative price of equipment 2.Motivation: Consumption-technology neutrality 3.Conceptual issues in empirical measurement 1.Mapping simple dynamic model to complicated world 2.Terms of trade as a form of technology 3.Manipulating input-output (I-O) tables 4.Data and empirical results: Bottom-up v. top-down 5.Interpretation
39 Motivation: In benchmark RBC model, consumption-specific technology shocks have no dynamic effects Suppose period utility is logarithmic U = ln(C) + v(1-L) Let A be multiplicative technology that affects only production of non- durable consumption
40 Note: This model is benchmark Greenwood-Hercowitz-Krusell model, with a different normalization of the two shocks We normalized on: Investment-specific technical change literature normalizes differently:
41 What is trade goods technology? The terms of trade We export in order to import Commodity exports are (intermediate) inputs into producing trade goods Output is imports plus net exports nominal value = export value P X X Real output = Goods we can import = P X X/P M
43 Does typical orthogonality assumption between neutral (consumption) and investment-specific technology hold? Correlations of final-use TFP Subscripts: J is overall investment, JE is equipment and software, C is nondurables and services consumption (1) Corr(dz J, dz C ) (2) Corr(dz JE, dz C ) (3) Corr(dz JE - dz C, dz C ) (4) Corr(dz JE - dz C, dz J )
44 Equipment investment technology and consumption technology are quite positively correlated… Correlations of BFK purified final-use technology (1) Corr(dz J, dz C ) (2) Corr(dz JE, dz C ) (3) Corr(dz JE - dz C, dz C ) (4) Corr(dz JE - dz C, dz J ) Subscripts: J is overall investment, JE is equipment and software, C is nondurables and services consumption.
45 Equipment technology improves reduce output and hours consumption technology improvements raise output Dep. Variables are growth rates of variables shown. Regressors are BFK final-sector technology shocks. Instruments are corresponding measures, with shocks to terms of trade, ag, mining, and govt. zeroed out. Std errors robust to heteroskedasticity and autocorrelation.
46 Table 6
47 Begin by using industry TFP Assume (for a start) that industry Solow (TFP) residual is the right measure of industry technical change Jorgenson data give us input-output (make) table B and the final-use vectors b
48 E&S and con. durables TFP rises faster than for nondurables and services, government, or structures Cumulated log change in final-use TFP